Spatially explicit modeling of disease surveillance in mixed oak-hardwood forests based on machine-learning algorithms. (1st July 2023)

• Record Type: Journal Article
• Authors: Ezzati, Sättar; Zenner, Eric K.; Pakdaman, Morteza; Naseri, Mohammad Hassan; Nikjoui, Marzieh; Ahmadi, Shahram
• Is Part Of: Journal of environmental management. Volume 337(2023)
• Publication Date: 2023-07-01
• Subjects: Data science -- Spatial planning -- Conservation -- Oak dieback -- Statistical analysis -- Quercus brantii; Environmental policy -- Periodicals; Environmental management -- Periodicals; Environment -- Periodicals; Ecology -- Periodicals; 363.705
• DOI: 10.1016/j.jenvman.2023.117714
• Languages: English
• ISSNs: 0301-4797

Abstract

Abstract: Incidences of disease, dieback, decline or mortality, some of which induced or enhanced by climate change, threaten the sustainability of forest stands in many ecosystems. Spatially explicit prediction of disease onset remains challenging, however, due to the involvement of several causative agents. In this paper, we developed a generic framework based on machine-learning algorithms and spatial analyses for landscape-level prediction of oak disease outbreaks caused by the charcoal fungus Biscogniauxia mediterranea in a mixed-oak forest of Mediterranean climate. For prediction, we used a set of fifteen causative factors as a cross-function of soil, site and stand-related predictors. A total of 80 sample plots, including 1134 affected trees, were surveyed and used for the modeling process at the 5600-ha landscape level of the southern Zagros, Iran, where the disease occurs in roughly 25% of forest lands. Ten machine learning algorithms were explored and the performance of each algorithm to predict oak disease outbreak was evaluated. The modeling framework used maximum entropy to remove the least influential variables and build the status-quo management scenario to which the results of the prediction models were compared. Results showed that the random forests algorithm (AUC = 0.96: Precision = 0.71: Accuracy = 0.90: F-Measure = 0.70) achieved significantly better results than the status-quo management (Precision = 0.13: Accuracy = 0.67: F-Measure = 0.12) and any other algorithm. Soil chemical properties (NPK, organic carbon and EC) and landform predictors (slope, distance to roads, and TWI) were major forecasters of oak disease outbreak identified by the random forest algorithm. Geostatistical analysis enabled the creation of a map that identified sites at higher risk of infestation, allowing epidemiologists and forest managers to find sites likely to be infested. Consequently, financial resources can be allocated and management practices such as sanitation felling treatments applied across large forest landscapes to minimize the risk of spread and severity to uninfested high-value trees on nearby or adjacent land zones that are in the early stage of epidemics. Highlights: Soil and landform attributes explain short-term presence of oak charcoal disease. Random forest (RF) algorithms outperform nine other machine learning algorithms. RF predicts 41% of Brant's oak at moderate disease intensity in southern Zagros. RF algorithm predicts oak dieback, decline or mortality with 96% accuracy. Machine-learning algorithms predict spatial charcoal disease intensity in Brant's oak.